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arXiv · 2605.26715

Image Feature Fusion-based Federated Client Unlearning (FCU)

Hangyi Shen·Yizhi Pan·Tiansuo Li·Weiqi Jiang·Guanqun Sun
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Abstract

Major data protection regulations all mention the "right to be forgotten," and that's what pushed federated unlearning (FU) techniques forward. But one stubborn issue remains: catastrophic forgetting--you erase the target knowledge, yet somehow you also end up throwing out essential retained knowledge, which then hurts the model's global generalization. To get a better balance between unlearning effectiveness and generalization ability, we propose something called Image Feature Fusion-based Federated Client Unlearning (IFF-FCU). The idea is to bring in a linear Image Feature Fusion mechanism (Mixup) that dynamically creates mixed samples, bridging the gap between forget-distribution and retain-distribution. What this strategy does isn't just deleting a few discrete data points--it theoretically widens and regularizes the forgetting boundary. We ran extensive experiments on medical imaging benchmarks (RSNA-ICH and ISIC2018), and the results show that our approach achieves reasonably good unlearning. For instance, on the ICH dataset, IFF-FCU achieves a highly competitive Error deviation from the retrained gold standard, demonstrating robust improvements over existing baselines.

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Hangyi Shen, Yizhi Pan, Tiansuo Li, Weiqi Jiang, Guanqun Sun. 2026-05-26. Image Feature Fusion-based Federated Client Unlearning (FCU). https://arxiv.org/abs/2605.26715

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